Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5899
Título: Short-term Load Forecasting Based on Load Profiling
Autor: Ramos, Sérgio
Soares, João
Vale, Zita
Ramos, Sandra
Palavras-chave: Load forecasting
Neural Networks
Exponential smoothing
Load profiling
Data: Jul-2013
Editora: IEEE
Relatório da Série N.º: PES;2013
Resumo: Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.
URI: http://hdl.handle.net/10400.22/5899
DOI: 10.1109/PESMG.2013.6672439
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6672439&queryText%3DShort-term+Load+Forecasting+Based+on+Load+Profiling
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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